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20112025
most citedRagas: Automated Evaluation of Retrieval Augmented Generation

77 citations · 353 across the 60 of their papers we have counts for

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Showing 2019 · cs.CLShow all

8 papers · 2 filters

cs.CL2019

Modelling Semantic Categories using Conceptual Neighborhood

Zied Bouraoui, Jose Camacho-Collados, Luis Espinosa-Anke +1

While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categorie…

cs.CL2019

Inducing Relational Knowledge from BERT

Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert

One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models…

cs.CL2019

Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings

Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1

Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in…

cs.CL2019★ 20 cited

Learning Household Task Knowledge from WikiHow Descriptions

Yilun Zhou, Julie A. Shah, Steven Schockaert

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly…

cs.CL2019★ 15 cited

On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning

Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1

Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the lang…

cs.CL2019

Relational Word Embeddings

Jose Camacho-Collados, Luis Espinosa-Anke, Steven Schockaert

While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In…

cs.CL2019★ 4 cited

Learning Cross-lingual Embeddings from Twitter via Distant Supervision

Jose Camacho-Collados, Yerai Doval, Eugenio Martínez-Cámara +3

Cross-lingual embeddings represent the meaning of words from different languages in the same vector space. Recent work has shown that it is possible to construct such representatio…

cs.CL2019★ 8 cited

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

Yilun Zhou, Steven Schockaert, Julie A. Shah

In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of informati…